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       "<h3>机器学习概述</h3>\n",
       "<span>\n",
       "1.1 机器学习算法分类<br>\n",
       "    监督学习<br> &nbsp &nbsp\n",
       "        目标值:类别 - 类别问题<br>&nbsp &nbsp\n",
       "        目标值:连续型的数据 - 回归问题<br>&nbsp &nbsp\n",
       "    目标值:无 - 无监督学习\n",
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       "<img src=\"1-01.png\" >\n"
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    "%%html\n",
    "<h3>机器学习概述</h3>\n",
    "<span>\n",
    "1.1 机器学习算法分类<br>\n",
    "    监督学习<br> &nbsp &nbsp\n",
    "        目标值:类别 - 类别问题<br>&nbsp &nbsp\n",
    "        目标值:连续型的数据 - 回归问题<br>&nbsp &nbsp\n",
    "    目标值:无 - 无监督学习\n",
    "</>\n",
    "<br>\n",
    "<img src=\"1-01.png\" >"
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   "execution_count": 27,
   "metadata": {},
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       "<span>\n",
       "1.2 问题总结<br>&nbsp &nbsp\n",
       "1.预测明天气温 - 回归问题<br>&nbsp &nbsp\n",
       "2.预测明天是阴、晴还是雨 - 分类问题<br>&nbsp &nbsp\n",
       "3.人脸年龄预测 - 回归/分类\n",
       "4.人脸识别 - 分类\n",
       "</span>\n"
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    "%%html\n",
    "<span>\n",
    "1.2 问题总结<br>&nbsp &nbsp\n",
    "1.预测明天气温 - 回归问题<br>&nbsp &nbsp\n",
    "2.预测明天是阴、晴还是雨 - 分类问题<br>&nbsp &nbsp\n",
    "3.人脸年龄预测 - 回归/分类\n",
    "4.人脸识别 - 分类\n",
    "</span>"
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   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
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       "<span>\n",
       "1.3 机器学习算法分类<br>\n",
       "1.监督学习(superived learning)(预测)<br>&nbsp&nbsp\n",
       "定义:输入数据是由输入特征值和目标值所组成,函数的输出可以是一个连续值(称为回归),或输出是有限的离散值(称为分类)<br>&nbsp&nbsp\n",
       "分类:k-近邻算法 贝叶斯算法 决策树 随机森林 逻辑回归<br>&nbsp&nbsp\n",
       "回归 线性回归 岭回归<br>\n",
       "2.无监督学习(unsuperivedlearning)<br>&nbsp&nbsp\n",
       "定义:输入数据是由输入特征值所组成<br>&nbsp&nbsp\n",
       "聚类 k-means\n",
       "</span>\n"
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    "%%html\n",
    "<span>\n",
    "1.3 机器学习算法分类<br>\n",
    "1.监督学习(superived learning)(预测)<br>&nbsp&nbsp\n",
    "定义:输入数据是由输入特征值和目标值所组成,函数的输出可以是一个连续值(称为回归),或输出是有限的离散值(称为分类)<br>&nbsp&nbsp\n",
    "分类:k-近邻算法 贝叶斯算法 决策树 随机森林 逻辑回归<br>&nbsp&nbsp\n",
    "回归 线性回归 岭回归<br>\n",
    "2.无监督学习(unsuperivedlearning)<br>&nbsp&nbsp\n",
    "定义:输入数据是由输入特征值所组成<br>&nbsp&nbsp\n",
    "聚类 k-means\n",
    "</span>"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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